1. Field of the Invention
The present invention relates generally to processing geospatial data, and more specifically, but not by way of limitation, to extracting geospatial attributes from geospatial datasets representing a particular geospatial object
2. Related Art
In general geospatial objects may be represented by geospatial datasets. Additionally, geospatial datasets may be broadly categorized into structured and non-structured types of geospatial datasets. Structured geospatial datasets include, but are not limited to, non-structured geospatial images and geospatial vector data. Geospatial images are images of the Earth's surface taken from the air or from space. Geospatial vector data may include any type of data that associates spatial attributes such as latitude and longitude coordinates to various sites on the Earth's surface. Geospatial vector data may also include non-spatial attributes like road names, house numbers, ZIP codes, ownership information, associated telephone numbers, tax information, valuation information, and so on. Non-structured types of geospatial datasets may include both spatial and non-spatial information, for example, photographs, RSS feeds, articles, and the like.
As alluded to above, geospatial datasets, regardless of the type, may be broken down into constituent geospatial attributes. Geospatial attributes may be categorized by name, location, type, and/or temporal data such as a date, but may also include additional categories such as zip code, phone number, and the like. It will be understood that, the geospatial attributes for a particular geospatial object may change over time. For example, when a business changes physical locations, the geospatial attributes of location, phone number, zip code, and the like will change.
In practice, geospatial attributes extracted from, for example, geospatial vector data, may be aligned or otherwise associated with corresponding geospatial imagery to produce content rich maps. Unfortunately, association of geospatial datasets with geospatial imagery without regard to the synchronicity between the geospatial datasets and the geospatial imagery may lead to maps with obvious errors. Stated otherwise, geospatial imagery is a visual representation of a particular geospatial location at a particular point in time when the geospatial imagery was captured. Likewise, geospatial vector data includes geospatial attributes representative of a particular geospatial object at a particular point in time when the vector data was created. Therefore, geospatial attributes of geospatial vector data may conflict with geospatial attributes of geospatial imagery supposedly corresponding to the exact same geospatial location depending on the time frame during which both the geospatial imagery and the geospatial vector data were created. For example, temporally newer geospatial vector data such as latitude and longitude coordinates corresponding to the location of a building may be erroneously combined with older geospatial imagery that, while showing the same latitude and longitude coordinates, fail to show the building because the building was not built at the time the geospatial image was captured. Deleterious geolocation errors such as these require independent verification of the actual location of the building and may cause users to question the reliability of the geolocation services.